Overview

Dataset statistics

Number of variables21
Number of observations16
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory176.0 B

Variable types

DateTime1
Numeric20

Alerts

dispute is highly correlated with total and 4 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 8 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 11 other fieldsHigh correlation
total is highly correlated with dispute and 8 other fieldsHigh correlation
gdp_ger is highly correlated with wars and 8 other fieldsHigh correlation
gdp_fra is highly correlated with dispute and 7 other fieldsHigh correlation
gdp_ita is highly correlated with dispute and 1 other fieldsHigh correlation
gdp_jpn is highly correlated with dispute and 5 other fieldsHigh correlation
gdp_can is highly correlated with non_violent_crises and 14 other fieldsHigh correlation
gdp_rus is highly correlated with wars and 9 other fieldsHigh correlation
gdp_usa is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_bra is highly correlated with dispute and 8 other fieldsHigh correlation
gdp_ind is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_mex is highly correlated with violent_crises and 12 other fieldsHigh correlation
gdp_zaf is highly correlated with total and 6 other fieldsHigh correlation
gdp_chn is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_wld is highly correlated with non_violent_crises and 10 other fieldsHigh correlation
dispute is highly correlated with total and 5 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 7 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 12 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 3 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 12 other fieldsHigh correlation
total is highly correlated with dispute and 10 other fieldsHigh correlation
gdp_ger is highly correlated with violent_crises and 12 other fieldsHigh correlation
gdp_fra is highly correlated with total and 8 other fieldsHigh correlation
gdp_ita is highly correlated with dispute and 2 other fieldsHigh correlation
gdp_jpn is highly correlated with dispute and 4 other fieldsHigh correlation
gdp_can is highly correlated with violent_crises and 13 other fieldsHigh correlation
gdp_rus is highly correlated with dispute and 11 other fieldsHigh correlation
gdp_usa is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_gbr is highly correlated with gdp_mexHigh correlation
gdp_bra is highly correlated with dispute and 11 other fieldsHigh correlation
gdp_ind is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_mex is highly correlated with violent_crises and 13 other fieldsHigh correlation
gdp_zaf is highly correlated with dispute and 11 other fieldsHigh correlation
gdp_chn is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gdp_wld is highly correlated with non_violent_crises and 13 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 3 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 6 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
total is highly correlated with violent_crises and 5 other fieldsHigh correlation
gdp_ger is highly correlated with gdp_fra and 6 other fieldsHigh correlation
gdp_fra is highly correlated with gdp_ger and 2 other fieldsHigh correlation
gdp_ita is highly correlated with gdp_fraHigh correlation
gdp_can is highly correlated with wars and 7 other fieldsHigh correlation
gdp_rus is highly correlated with total and 6 other fieldsHigh correlation
gdp_usa is highly correlated with violent_crises and 5 other fieldsHigh correlation
gdp_bra is highly correlated with wars and 5 other fieldsHigh correlation
gdp_ind is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
gdp_mex is highly correlated with total and 6 other fieldsHigh correlation
gdp_zaf is highly correlated with gdp_can and 2 other fieldsHigh correlation
gdp_chn is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
gdp_wld is highly correlated with violent_crises and 7 other fieldsHigh correlation
year is highly correlated with dispute and 19 other fieldsHigh correlation
dispute is highly correlated with year and 15 other fieldsHigh correlation
non_violent_crises is highly correlated with year and 13 other fieldsHigh correlation
violent_crises is highly correlated with year and 12 other fieldsHigh correlation
limited_wars is highly correlated with year and 4 other fieldsHigh correlation
wars is highly correlated with year and 6 other fieldsHigh correlation
total is highly correlated with year and 12 other fieldsHigh correlation
gdp_ger is highly correlated with year and 10 other fieldsHigh correlation
gdp_fra is highly correlated with year and 15 other fieldsHigh correlation
gdp_ita is highly correlated with year and 13 other fieldsHigh correlation
gdp_jpn is highly correlated with year and 12 other fieldsHigh correlation
gdp_can is highly correlated with year and 15 other fieldsHigh correlation
gdp_rus is highly correlated with year and 12 other fieldsHigh correlation
gdp_usa is highly correlated with year and 14 other fieldsHigh correlation
gdp_gbr is highly correlated with year and 9 other fieldsHigh correlation
gdp_bra is highly correlated with year and 15 other fieldsHigh correlation
gdp_ind is highly correlated with year and 16 other fieldsHigh correlation
gdp_mex is highly correlated with year and 12 other fieldsHigh correlation
gdp_zaf is highly correlated with year and 11 other fieldsHigh correlation
gdp_chn is highly correlated with year and 16 other fieldsHigh correlation
gdp_wld is highly correlated with year and 12 other fieldsHigh correlation
year has unique values Unique
violent_crises has unique values Unique
total has unique values Unique
gdp_ger has unique values Unique
gdp_fra has unique values Unique
gdp_ita has unique values Unique
gdp_jpn has unique values Unique
gdp_can has unique values Unique
gdp_rus has unique values Unique
gdp_usa has unique values Unique
gdp_gbr has unique values Unique
gdp_bra has unique values Unique
gdp_ind has unique values Unique
gdp_mex has unique values Unique
gdp_zaf has unique values Unique
gdp_chn has unique values Unique
gdp_wld has unique values Unique

Reproduction

Analysis started2022-07-04 11:37:21.351436
Analysis finished2022-07-04 11:38:02.478000
Duration41.13 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
Minimum1970-01-01 00:00:00.000002
Maximum1970-01-01 00:00:00.000002
2022-07-04T13:38:02.565818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:02.665841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

dispute
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.375
Minimum63
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:02.763863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile66.75
Q170.5
median80.5
Q397.5
95-th percentile107
Maximum107
Range44
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.61996586
Coefficient of variation (CV)0.1851255213
Kurtosis-1.540707544
Mean84.375
Median Absolute Deviation (MAD)12.5
Skewness0.2549791868
Sum1350
Variance243.9833333
MonotonicityNot monotonic
2022-07-04T13:38:02.859885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1072
12.5%
682
12.5%
631
 
6.2%
721
 
6.2%
791
 
6.2%
821
 
6.2%
951
 
6.2%
1061
 
6.2%
991
 
6.2%
971
 
6.2%
Other values (4)4
25.0%
ValueCountFrequency (%)
631
6.2%
682
12.5%
691
6.2%
711
6.2%
721
6.2%
771
6.2%
791
6.2%
821
6.2%
901
6.2%
951
6.2%
ValueCountFrequency (%)
1072
12.5%
1061
6.2%
991
6.2%
971
6.2%
951
6.2%
901
6.2%
821
6.2%
791
6.2%
771
6.2%
721
6.2%

non_violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.5
Minimum70
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:02.952905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile71.5
Q182.75
median88
Q3109.5
95-th percentile127
Maximum130
Range60
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation18.82197297
Coefficient of variation (CV)0.1991743172
Kurtosis-0.6739379008
Mean94.5
Median Absolute Deviation (MAD)8.5
Skewness0.7089213253
Sum1512
Variance354.2666667
MonotonicityNot monotonic
2022-07-04T13:38:03.053929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
882
 
12.5%
931
 
6.2%
1141
 
6.2%
1261
 
6.2%
1301
 
6.2%
1181
 
6.2%
1081
 
6.2%
871
 
6.2%
851
 
6.2%
821
 
6.2%
Other values (5)5
31.2%
ValueCountFrequency (%)
701
6.2%
721
6.2%
771
6.2%
821
6.2%
831
6.2%
851
6.2%
871
6.2%
882
12.5%
911
6.2%
931
6.2%
ValueCountFrequency (%)
1301
6.2%
1261
6.2%
1181
6.2%
1141
6.2%
1081
6.2%
931
6.2%
911
6.2%
882
12.5%
871
6.2%
851
6.2%

violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.9375
Minimum90
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:03.153951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile99
Q1109.25
median165.5
Q3180.25
95-th percentile188.5
Maximum190
Range100
Interquartile range (IQR)71

Descriptive statistics

Standard deviation36.24080343
Coefficient of variation (CV)0.2401047018
Kurtosis-1.457733166
Mean150.9375
Median Absolute Deviation (MAD)20
Skewness-0.5732856418
Sum2415
Variance1313.395833
MonotonicityNot monotonic
2022-07-04T13:38:03.319989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
901
 
6.2%
1041
 
6.2%
1071
 
6.2%
1021
 
6.2%
1101
 
6.2%
1391
 
6.2%
1551
 
6.2%
1771
 
6.2%
1781
 
6.2%
1811
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
901
6.2%
1021
6.2%
1041
6.2%
1071
6.2%
1101
6.2%
1391
6.2%
1551
6.2%
1581
6.2%
1731
6.2%
1771
6.2%
ValueCountFrequency (%)
1901
6.2%
1881
6.2%
1831
6.2%
1811
6.2%
1801
6.2%
1781
6.2%
1771
6.2%
1731
6.2%
1581
6.2%
1551
6.2%

limited_wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.1875
Minimum16
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:03.417011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18.25
Q121.75
median25
Q326
95-th percentile30.25
Maximum31
Range15
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.16683333
Coefficient of variation (CV)0.172272179
Kurtosis-0.2284627711
Mean24.1875
Median Absolute Deviation (MAD)2.5
Skewness-0.1575500079
Sum387
Variance17.3625
MonotonicityNot monotonic
2022-07-04T13:38:03.517033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
254
25.0%
262
12.5%
302
12.5%
192
12.5%
221
 
6.2%
311
 
6.2%
241
 
6.2%
211
 
6.2%
161
 
6.2%
231
 
6.2%
ValueCountFrequency (%)
161
 
6.2%
192
12.5%
211
 
6.2%
221
 
6.2%
231
 
6.2%
241
 
6.2%
254
25.0%
262
12.5%
302
12.5%
311
 
6.2%
ValueCountFrequency (%)
311
 
6.2%
302
12.5%
262
12.5%
254
25.0%
241
 
6.2%
231
 
6.2%
221
 
6.2%
211
 
6.2%
192
12.5%
161
 
6.2%

wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:03.613055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17.5
median17
Q319.25
95-th percentile20.25
Maximum21
Range19
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.582805886
Coefficient of variation (CV)0.4702004204
Kurtosis-1.406969764
Mean14
Median Absolute Deviation (MAD)3
Skewness-0.5785301457
Sum224
Variance43.33333333
MonotonicityNot monotonic
2022-07-04T13:38:03.712077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
63
18.8%
203
18.8%
193
18.8%
21
 
6.2%
91
 
6.2%
81
 
6.2%
181
 
6.2%
161
 
6.2%
151
 
6.2%
211
 
6.2%
ValueCountFrequency (%)
21
 
6.2%
63
18.8%
81
 
6.2%
91
 
6.2%
151
 
6.2%
161
 
6.2%
181
 
6.2%
193
18.8%
203
18.8%
211
 
6.2%
ValueCountFrequency (%)
211
 
6.2%
203
18.8%
193
18.8%
181
 
6.2%
161
 
6.2%
151
 
6.2%
91
 
6.2%
81
 
6.2%
63
18.8%
21
 
6.2%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368
Minimum274
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:03.810099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile313
Q1356.75
median369
Q3391.25
95-th percentile412
Maximum418
Range144
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation35.55090247
Coefficient of variation (CV)0.09660571324
Kurtosis2.194012998
Mean368
Median Absolute Deviation (MAD)17
Skewness-1.051035019
Sum5888
Variance1263.866667
MonotonicityNot monotonic
2022-07-04T13:38:03.912122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2741
 
6.2%
3261
 
6.2%
3441
 
6.2%
3531
 
6.2%
3681
 
6.2%
3701
 
6.2%
3871
 
6.2%
4051
 
6.2%
4181
 
6.2%
4101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2741
6.2%
3261
6.2%
3441
6.2%
3531
6.2%
3581
6.2%
3591
6.2%
3651
6.2%
3681
6.2%
3701
6.2%
3711
6.2%
ValueCountFrequency (%)
4181
6.2%
4101
6.2%
4051
6.2%
4041
6.2%
3871
6.2%
3761
6.2%
3711
6.2%
3701
6.2%
3681
6.2%
3651
6.2%

gdp_ger
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.559561875 × 1012
Minimum2.84686 × 1012
Maximum3.97729 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:04.016146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.84686 × 1012
5-th percentile2.95774 × 1012
Q13.4083625 × 1012
median3.608995 × 1012
Q33.773585 × 1012
95-th percentile3.91114 × 1012
Maximum3.97729 × 1012
Range1.13043 × 1012
Interquartile range (IQR)3.652225 × 1011

Descriptive statistics

Standard deviation3.197868537 × 1011
Coefficient of variation (CV)0.08983882425
Kurtosis0.3631186069
Mean3.559561875 × 1012
Median Absolute Deviation (MAD)2.0353 × 1011
Skewness-0.8675955451
Sum5.695299 × 1013
Variance1.022636318 × 1023
MonotonicityNot monotonic
2022-07-04T13:38:04.118169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.84686 × 10121
 
6.2%
2.9947 × 10121
 
6.2%
3.42558 × 10121
 
6.2%
3.74526 × 10121
 
6.2%
3.41126 × 10121
 
6.2%
3.39967 × 10121
 
6.2%
3.74931 × 10121
 
6.2%
3.52714 × 10121
 
6.2%
3.7338 × 10121
 
6.2%
3.88909 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.84686 × 10121
6.2%
2.9947 × 10121
6.2%
3.35759 × 10121
6.2%
3.39967 × 10121
6.2%
3.41126 × 10121
6.2%
3.42558 × 10121
6.2%
3.46985 × 10121
6.2%
3.52714 × 10121
6.2%
3.69085 × 10121
6.2%
3.7338 × 10121
6.2%
ValueCountFrequency (%)
3.97729 × 10121
6.2%
3.88909 × 10121
6.2%
3.88833 × 10121
6.2%
3.84641 × 10121
6.2%
3.74931 × 10121
6.2%
3.74526 × 10121
6.2%
3.7338 × 10121
6.2%
3.69085 × 10121
6.2%
3.52714 × 10121
6.2%
3.46985 × 10121
6.2%

gdp_fra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.64553625 × 1012
Minimum2.19695 × 1012
Maximum2.9303 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:04.217191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.19695 × 1012
5-th percentile2.2896425 × 1012
Q12.5646025 × 1012
median2.67213 × 1012
Q32.79619 × 1012
95-th percentile2.881445 × 1012
Maximum2.9303 × 1012
Range7.3335 × 1011
Interquartile range (IQR)2.315875 × 1011

Descriptive statistics

Standard deviation2.026647068 × 1011
Coefficient of variation (CV)0.07660628609
Kurtosis0.2181928739
Mean2.64553625 × 1012
Median Absolute Deviation (MAD)1.2929 × 1011
Skewness-0.7978294731
Sum4.232858 × 1013
Variance4.10729834 × 1022
MonotonicityNot monotonic
2022-07-04T13:38:04.318214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.19695 × 10121
 
6.2%
2.32054 × 10121
 
6.2%
2.66059 × 10121
 
6.2%
2.9303 × 10121
 
6.2%
2.70089 × 10121
 
6.2%
2.64519 × 10121
 
6.2%
2.86516 × 10121
 
6.2%
2.68367 × 10121
 
6.2%
2.81188 × 10121
 
6.2%
2.85596 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.19695 × 10121
6.2%
2.32054 × 10121
6.2%
2.43919 × 10121
6.2%
2.47296 × 10121
6.2%
2.59515 × 10121
6.2%
2.63032 × 10121
6.2%
2.64519 × 10121
6.2%
2.66059 × 10121
6.2%
2.68367 × 10121
6.2%
2.70089 × 10121
6.2%
ValueCountFrequency (%)
2.9303 × 10121
6.2%
2.86516 × 10121
6.2%
2.85596 × 10121
6.2%
2.81188 × 10121
6.2%
2.79096 × 10121
6.2%
2.72887 × 10121
6.2%
2.70089 × 10121
6.2%
2.68367 × 10121
6.2%
2.66059 × 10121
6.2%
2.64519 × 10121
6.2%

gdp_ita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.069810625 × 1012
Minimum1.83664 × 1012
Maximum2.40866 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:04.418236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.83664 × 1012
5-th percentile1.852825 × 1012
Q11.93434 × 1012
median2.089445 × 1012
Q32.17149 × 1012
95-th percentile2.3234075 × 1012
Maximum2.40866 × 1012
Range5.7202 × 1011
Interquartile range (IQR)2.3715 × 1011

Descriptive statistics

Standard deviation1.674777545 × 1011
Coefficient of variation (CV)0.08091453028
Kurtosis-0.6242644172
Mean2.069810625 × 1012
Median Absolute Deviation (MAD)1.2565 × 1011
Skewness0.2994897783
Sum3.311697 × 1013
Variance2.804879825 × 1022
MonotonicityNot monotonic
2022-07-04T13:38:04.519260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.85822 × 10121
 
6.2%
1.94955 × 10121
 
6.2%
2.2131 × 10121
 
6.2%
2.40866 × 10121
 
6.2%
2.19993 × 10121
 
6.2%
2.1361 × 10121
 
6.2%
2.29499 × 10121
 
6.2%
2.08696 × 10121
 
6.2%
2.14192 × 10121
 
6.2%
2.16201 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.83664 × 10121
6.2%
1.85822 × 10121
6.2%
1.87707 × 10121
6.2%
1.88871 × 10121
6.2%
1.94955 × 10121
6.2%
1.9618 × 10121
6.2%
2.00938 × 10121
6.2%
2.08696 × 10121
6.2%
2.09193 × 10121
6.2%
2.1361 × 10121
6.2%
ValueCountFrequency (%)
2.40866 × 10121
6.2%
2.29499 × 10121
6.2%
2.2131 × 10121
6.2%
2.19993 × 10121
6.2%
2.16201 × 10121
6.2%
2.14192 × 10121
6.2%
2.1361 × 10121
6.2%
2.09193 × 10121
6.2%
2.08696 × 10121
6.2%
2.00938 × 10121
6.2%

gdp_jpn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.150364375 × 1012
Minimum4.44493 × 1012
Maximum6.27236 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:04.695299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.44493 × 1012
5-th percentile4.546045 × 1012
Q14.88061 × 1012
median5.047325 × 1012
Q35.23162 × 1012
95-th percentile6.2429525 × 1012
Maximum6.27236 × 1012
Range1.82743 × 1012
Interquartile range (IQR)3.5101 × 1011

Descriptive statistics

Standard deviation5.296724972 × 1011
Coefficient of variation (CV)0.1028417523
Kurtosis0.7960793162
Mean5.150364375 × 1012
Median Absolute Deviation (MAD)1.9043 × 1011
Skewness1.099057384
Sum8.240583 × 1013
Variance2.805529543 × 1023
MonotonicityNot monotonic
2022-07-04T13:38:04.799323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.83147 × 10121
 
6.2%
4.60166 × 10121
 
6.2%
4.57975 × 10121
 
6.2%
5.10668 × 10121
 
6.2%
5.28949 × 10121
 
6.2%
5.75907 × 10121
 
6.2%
6.23315 × 10121
 
6.2%
6.27236 × 10121
 
6.2%
5.21233 × 10121
 
6.2%
4.89699 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.44493 × 10121
6.2%
4.57975 × 10121
6.2%
4.60166 × 10121
6.2%
4.83147 × 10121
6.2%
4.89699 × 10121
6.2%
4.93084 × 10121
6.2%
5.00368 × 10121
6.2%
5.03689 × 10121
6.2%
5.05776 × 10121
6.2%
5.10668 × 10121
6.2%
ValueCountFrequency (%)
6.27236 × 10121
6.2%
6.23315 × 10121
6.2%
5.75907 × 10121
6.2%
5.28949 × 10121
6.2%
5.21233 × 10121
6.2%
5.14878 × 10121
6.2%
5.10668 × 10121
6.2%
5.05776 × 10121
6.2%
5.03689 × 10121
6.2%
5.00368 × 10121
6.2%

gdp_can
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.60167125 × 1012
Minimum1.17311 × 1012
Maximum1.8466 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:04.900346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.17311 × 1012
5-th percentile1.2827225 × 1012
Q11.5131975 × 1012
median1.63138 × 1012
Q31.7548475 × 1012
95-th percentile1.8329275 × 1012
Maximum1.8466 × 1012
Range6.7349 × 1011
Interquartile range (IQR)2.4165 × 1011

Descriptive statistics

Standard deviation1.95215157 × 1011
Coefficient of variation (CV)0.1218821634
Kurtosis-0.06889749863
Mean1.60167125 × 1012
Median Absolute Deviation (MAD)1.36295 × 1011
Skewness-0.7082639053
Sum2.562674 × 1013
Variance3.810895751 × 1022
MonotonicityNot monotonic
2022-07-04T13:38:05.003369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.17311 × 10121
 
6.2%
1.31926 × 10121
 
6.2%
1.46882 × 10121
 
6.2%
1.55299 × 10121
 
6.2%
1.37463 × 10121
 
6.2%
1.61734 × 10121
 
6.2%
1.79333 × 10121
 
6.2%
1.82837 × 10121
 
6.2%
1.8466 × 10121
 
6.2%
1.80575 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.17311 × 10121
6.2%
1.31926 × 10121
6.2%
1.37463 × 10121
6.2%
1.46882 × 10121
6.2%
1.52799 × 10121
6.2%
1.55299 × 10121
6.2%
1.55651 × 10121
6.2%
1.61734 × 10121
6.2%
1.64542 × 10121
6.2%
1.64927 × 10121
6.2%
ValueCountFrequency (%)
1.8466 × 10121
6.2%
1.82837 × 10121
6.2%
1.80575 × 10121
6.2%
1.79333 × 10121
6.2%
1.74202 × 10121
6.2%
1.72533 × 10121
6.2%
1.64927 × 10121
6.2%
1.64542 × 10121
6.2%
1.61734 × 10121
6.2%
1.55651 × 10121
6.2%

gdp_rus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.569422375 × 1012
Minimum7.64017 × 1011
Maximum2.29247 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:05.102391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7.64017 × 1011
5-th percentile9.334525 × 1011
Q11.29398 × 1012
median1.54956 × 1012
Q31.77707 × 1012
95-th percentile2.2293425 × 1012
Maximum2.29247 × 1012
Range1.528453 × 1012
Interquartile range (IQR)4.8309 × 1011

Descriptive statistics

Standard deviation4.276200303 × 1011
Coefficient of variation (CV)0.2724696915
Kurtosis-0.3957895742
Mean1.569422375 × 1012
Median Absolute Deviation (MAD)2.6131 × 1011
Skewness0.05328737967
Sum2.5110758 × 1013
Variance1.828588903 × 1023
MonotonicityNot monotonic
2022-07-04T13:38:05.203414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.64017 × 10111
 
6.2%
9.89931 × 10111
 
6.2%
1.29971 × 10121
 
6.2%
1.66085 × 10121
 
6.2%
1.22264 × 10121
 
6.2%
1.52492 × 10121
 
6.2%
2.04593 × 10121
 
6.2%
2.2083 × 10121
 
6.2%
2.29247 × 10121
 
6.2%
2.05924 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
7.64017 × 10111
6.2%
9.89931 × 10111
6.2%
1.22264 × 10121
6.2%
1.27679 × 10121
6.2%
1.29971 × 10121
6.2%
1.36348 × 10121
6.2%
1.4835 × 10121
6.2%
1.52492 × 10121
6.2%
1.5742 × 10121
6.2%
1.65733 × 10121
6.2%
ValueCountFrequency (%)
2.29247 × 10121
6.2%
2.2083 × 10121
6.2%
2.05924 × 10121
6.2%
2.04593 × 10121
6.2%
1.68745 × 10121
6.2%
1.66085 × 10121
6.2%
1.65733 × 10121
6.2%
1.5742 × 10121
6.2%
1.52492 × 10121
6.2%
1.4835 × 10121
6.2%

gdp_usa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.69395625 × 1013
Minimum1.30366 × 1013
Maximum2.14332 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:05.305438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.30366 × 1013
5-th percentile1.36201 × 1013
Q11.4647575 × 1013
median1.64909 × 1013
Q31.8944575 × 1013
95-th percentile2.107305 × 1013
Maximum2.14332 × 1013
Range8.3966 × 1012
Interquartile range (IQR)4.297 × 1012

Descriptive statistics

Standard deviation2.714463951 × 1012
Coefficient of variation (CV)0.1602440412
Kurtosis-1.220549636
Mean1.69395625 × 1013
Median Absolute Deviation (MAD)2.0405 × 1012
Skewness0.3369507621
Sum2.71033 × 1014
Variance7.368314541 × 1024
MonotonicityNot monotonic
2022-07-04T13:38:05.404460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.30366 × 10131
 
6.2%
1.38146 × 10131
 
6.2%
1.44519 × 10131
 
6.2%
1.47128 × 10131
 
6.2%
1.44489 × 10131
 
6.2%
1.49921 × 10131
 
6.2%
1.55426 × 10131
 
6.2%
1.6197 × 10131
 
6.2%
1.67848 × 10131
 
6.2%
1.75272 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.30366 × 10131
6.2%
1.38146 × 10131
6.2%
1.44489 × 10131
6.2%
1.44519 × 10131
6.2%
1.47128 × 10131
6.2%
1.49921 × 10131
6.2%
1.55426 × 10131
6.2%
1.6197 × 10131
6.2%
1.67848 × 10131
6.2%
1.75272 × 10131
6.2%
ValueCountFrequency (%)
2.14332 × 10131
6.2%
2.0953 × 10131
6.2%
2.06119 × 10131
6.2%
1.9543 × 10131
6.2%
1.87451 × 10131
6.2%
1.82383 × 10131
6.2%
1.75272 × 10131
6.2%
1.67848 × 10131
6.2%
1.6197 × 10131
6.2%
1.55426 × 10131
6.2%

gdp_gbr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.776629375 × 1012
Minimum2.4258 × 1012
Maximum3.10618 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:05.503482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.4258 × 1012
5-th percentile2.4747825 × 1012
Q12.6929875 × 1012
median2.741325 × 1012
Q32.9103125 × 1012
95-th percentile3.0919225 × 1012
Maximum3.10618 × 1012
Range6.8038 × 1011
Interquartile range (IQR)2.17325 × 1011

Descriptive statistics

Standard deviation1.957917678 × 1011
Coefficient of variation (CV)0.07051418874
Kurtosis-0.4517141964
Mean2.776629375 × 1012
Median Absolute Deviation (MAD)1.48405 × 1011
Skewness-0.02195469218
Sum4.442607 × 1013
Variance3.833441634 × 1022
MonotonicityNot monotonic
2022-07-04T13:38:05.606505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.54483 × 10121
 
6.2%
2.71706 × 10121
 
6.2%
3.10618 × 10121
 
6.2%
2.93888 × 10121
 
6.2%
2.4258 × 10121
 
6.2%
2.49111 × 10121
 
6.2%
2.67489 × 10121
 
6.2%
2.71916 × 10121
 
6.2%
2.80329 × 10121
 
6.2%
3.08717 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.4258 × 10121
6.2%
2.49111 × 10121
6.2%
2.54483 × 10121
6.2%
2.67489 × 10121
6.2%
2.69902 × 10121
6.2%
2.71706 × 10121
6.2%
2.71916 × 10121
6.2%
2.72285 × 10121
6.2%
2.7598 × 10121
6.2%
2.80329 × 10121
6.2%
ValueCountFrequency (%)
3.10618 × 10121
6.2%
3.08717 × 10121
6.2%
2.95657 × 10121
6.2%
2.93888 × 10121
6.2%
2.90079 × 10121
6.2%
2.87867 × 10121
6.2%
2.80329 × 10121
6.2%
2.7598 × 10121
6.2%
2.72285 × 10121
6.2%
2.71916 × 10121
6.2%

gdp_bra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.867450875 × 1012
Minimum8.91634 × 1011
Maximum2.61616 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:05.711529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.91634 × 1011
5-th percentile1.053631 × 1012
Q11.6114325 × 1012
median1.840015 × 1012
Q32.27064 × 1012
95-th percentile2.508655 × 1012
Maximum2.61616 × 1012
Range1.724526 × 1012
Interquartile range (IQR)6.592075 × 1011

Descriptive statistics

Standard deviation5.031133324 × 1011
Coefficient of variation (CV)0.2694118165
Kurtosis-0.5446859722
Mean1.867450875 × 1012
Median Absolute Deviation (MAD)3.82055 × 1011
Skewness-0.2720249931
Sum2.9879214 × 1013
Variance2.531230252 × 1023
MonotonicityNot monotonic
2022-07-04T13:38:05.810551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.91634 × 10111
 
6.2%
1.10763 × 10121
 
6.2%
1.39711 × 10121
 
6.2%
1.69586 × 10121
 
6.2%
1.667 × 10121
 
6.2%
2.20884 × 10121
 
6.2%
2.61616 × 10121
 
6.2%
2.46523 × 10121
 
6.2%
2.47282 × 10121
 
6.2%
2.45604 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.91634 × 10111
6.2%
1.10763 × 10121
6.2%
1.39711 × 10121
6.2%
1.44473 × 10121
6.2%
1.667 × 10121
6.2%
1.69586 × 10121
6.2%
1.79569 × 10121
6.2%
1.80221 × 10121
6.2%
1.87782 × 10121
6.2%
1.91693 × 10121
6.2%
ValueCountFrequency (%)
2.61616 × 10121
6.2%
2.47282 × 10121
6.2%
2.46523 × 10121
6.2%
2.45604 × 10121
6.2%
2.20884 × 10121
6.2%
2.06351 × 10121
6.2%
1.91693 × 10121
6.2%
1.87782 × 10121
6.2%
1.80221 × 10121
6.2%
1.79569 × 10121
6.2%

gdp_ind
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.87637825 × 1012
Minimum8.20382 × 1011
Maximum2.8705 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:05.978589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.20382 × 1011
5-th percentile9.102905 × 1011
Q11.3106025 × 1012
median1.84218 × 1012
Q32.3839675 × 1012
95-th percentile2.7434575 × 1012
Maximum2.8705 × 1012
Range2.050118 × 1012
Interquartile range (IQR)1.073365 × 1012

Descriptive statistics

Standard deviation6.508458556 × 1011
Coefficient of variation (CV)0.3468628223
Kurtosis-1.09546485
Mean1.87637825 × 1012
Median Absolute Deviation (MAD)5.62865 × 1011
Skewness-0.04888671823
Sum3.0022052 × 1013
Variance4.236003277 × 1023
MonotonicityNot monotonic
2022-07-04T13:38:06.075612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.20382 × 10111
 
6.2%
9.4026 × 10111
 
6.2%
1.21674 × 10121
 
6.2%
1.1989 × 10121
 
6.2%
1.34189 × 10121
 
6.2%
1.67562 × 10121
 
6.2%
1.82305 × 10121
 
6.2%
1.82764 × 10121
 
6.2%
1.85672 × 10121
 
6.2%
2.03913 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.20382 × 10111
6.2%
9.4026 × 10111
6.2%
1.1989 × 10121
6.2%
1.21674 × 10121
6.2%
1.34189 × 10121
6.2%
1.67562 × 10121
6.2%
1.82305 × 10121
6.2%
1.82764 × 10121
6.2%
1.85672 × 10121
6.2%
2.03913 × 10121
6.2%
ValueCountFrequency (%)
2.8705 × 10121
6.2%
2.70111 × 10121
6.2%
2.66025 × 10121
6.2%
2.65147 × 10121
6.2%
2.2948 × 10121
6.2%
2.10359 × 10121
6.2%
2.03913 × 10121
6.2%
1.85672 × 10121
6.2%
1.82764 × 10121
6.2%
1.82305 × 10121
6.2%

gdp_mex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.119987375 × 1012
Minimum8.77476 × 1011
Maximum1.31535 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:06.172633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.77476 × 1011
5-th percentile8.9440275 × 1011
Q11.056525 × 1012
median1.13445 × 1012
Q31.20642 × 1012
95-th percentile1.2846675 × 1012
Maximum1.31535 × 1012
Range4.37874 × 1011
Interquartile range (IQR)1.49895 × 1011

Descriptive statistics

Standard deviation1.290285159 × 1011
Coefficient of variation (CV)0.1152053307
Kurtosis-0.4807950413
Mean1.119987375 × 1012
Median Absolute Deviation (MAD)7.92 × 1010
Skewness-0.4303968422
Sum1.7919798 × 1013
Variance1.664835791 × 1022
MonotonicityNot monotonic
2022-07-04T13:38:06.273657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.77476 × 10111
 
6.2%
9.75387 × 10111
 
6.2%
1.0527 × 10121
 
6.2%
1.10999 × 10121
 
6.2%
9.00045 × 10111
 
6.2%
1.0578 × 10121
 
6.2%
1.18049 × 10121
 
6.2%
1.20109 × 10121
 
6.2%
1.27444 × 10121
 
6.2%
1.31535 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.77476 × 10111
6.2%
9.00045 × 10111
6.2%
9.75387 × 10111
6.2%
1.0527 × 10121
6.2%
1.0578 × 10121
6.2%
1.07392 × 10121
6.2%
1.07849 × 10121
6.2%
1.10999 × 10121
6.2%
1.15891 × 10121
6.2%
1.17187 × 10121
6.2%
ValueCountFrequency (%)
1.31535 × 10121
6.2%
1.27444 × 10121
6.2%
1.26943 × 10121
6.2%
1.22241 × 10121
6.2%
1.20109 × 10121
6.2%
1.18049 × 10121
6.2%
1.17187 × 10121
6.2%
1.15891 × 10121
6.2%
1.10999 × 10121
6.2%
1.07849 × 10121
6.2%

gdp_zaf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.65231625 × 1011
Minimum2.88868 × 1011
Maximum4.58202 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:06.372679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.88868 × 1011
5-th percentile3.0011275 × 1011
Q13.2821125 × 1011
median3.639545 × 1011
Q34.01875 × 1011
95-th percentile4.4035125 × 1011
Maximum4.58202 × 1011
Range1.69334 × 1011
Interquartile range (IQR)7.366375 × 1010

Descriptive statistics

Standard deviation4.985682399 × 1010
Coefficient of variation (CV)0.1365074122
Kurtosis-0.9669365717
Mean3.65231625 × 1011
Median Absolute Deviation (MAD)3.865 × 1010
Skewness0.2618885705
Sum5.843706 × 1012
Variance2.485702899 × 1021
MonotonicityNot monotonic
2022-07-04T13:38:06.474702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.88868 × 10111
 
6.2%
3.03861 × 10111
 
6.2%
3.33075 × 10111
 
6.2%
3.16132 × 10111
 
6.2%
3.29753 × 10111
 
6.2%
4.17365 × 10111
 
6.2%
4.58202 × 10111
 
6.2%
4.34401 × 10111
 
6.2%
4.00886 × 10111
 
6.2%
3.81199 × 10111
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.88868 × 10111
6.2%
3.03861 × 10111
6.2%
3.16132 × 10111
6.2%
3.23586 × 10111
6.2%
3.29753 × 10111
6.2%
3.33075 × 10111
6.2%
3.35442 × 10111
6.2%
3.4671 × 10111
6.2%
3.81199 × 10111
6.2%
3.81449 × 10111
6.2%
ValueCountFrequency (%)
4.58202 × 10111
6.2%
4.34401 × 10111
6.2%
4.17365 × 10111
6.2%
4.04842 × 10111
6.2%
4.00886 × 10111
6.2%
3.87935 × 10111
6.2%
3.81449 × 10111
6.2%
3.81199 × 10111
6.2%
3.4671 × 10111
6.2%
3.35442 × 10111
6.2%

gdp_chn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.625259375 × 1012
Minimum2.28597 × 1012
Maximum1.47227 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:06.575725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.28597 × 1012
5-th percentile2.63559 × 1012
Q14.9748525 × 1012
median9.05132 × 1012
Q31.1502575 × 1013
95-th percentile1.43906 × 1013
Maximum1.47227 × 1013
Range1.243673 × 1013
Interquartile range (IQR)6.5277225 × 1012

Descriptive statistics

Standard deviation4.197298854 × 1012
Coefficient of variation (CV)0.4866287113
Kurtosis-1.338652603
Mean8.625259375 × 1012
Median Absolute Deviation (MAD)3.60435 × 1012
Skewness-0.07379734009
Sum1.3800415 × 1014
Variance1.761731767 × 1025
MonotonicityStrictly increasing
2022-07-04T13:38:06.679748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.28597 × 10121
 
6.2%
2.75213 × 10121
 
6.2%
3.55034 × 10121
 
6.2%
4.59431 × 10121
 
6.2%
5.1017 × 10121
 
6.2%
6.08716 × 10121
 
6.2%
7.5515 × 10121
 
6.2%
8.53223 × 10121
 
6.2%
9.57041 × 10121
 
6.2%
1.04757 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.28597 × 10121
6.2%
2.75213 × 10121
6.2%
3.55034 × 10121
6.2%
4.59431 × 10121
6.2%
5.1017 × 10121
6.2%
6.08716 × 10121
6.2%
7.5515 × 10121
6.2%
8.53223 × 10121
6.2%
9.57041 × 10121
6.2%
1.04757 × 10131
6.2%
ValueCountFrequency (%)
1.47227 × 10131
6.2%
1.42799 × 10131
6.2%
1.38948 × 10131
6.2%
1.23104 × 10131
6.2%
1.12333 × 10131
6.2%
1.10616 × 10131
6.2%
1.04757 × 10131
6.2%
9.57041 × 10121
6.2%
8.53223 × 10121
6.2%
7.5515 × 10121
6.2%

gdp_wld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1655825 × 1013
Minimum4.77779 × 1013
Maximum8.75681 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:38:06.781771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.77779 × 1013
5-th percentile5.07785 × 1013
Q16.3248875 × 1013
median7.521435 × 1013
Q37.9987775 × 1013
95-th percentile8.659775 × 1013
Maximum8.75681 × 1013
Range3.97902 × 1013
Interquartile range (IQR)1.67389 × 1013

Descriptive statistics

Standard deviation1.217141227 × 1013
Coefficient of variation (CV)0.1698593558
Kurtosis-0.6033356899
Mean7.1655825 × 1013
Median Absolute Deviation (MAD)9.12325 × 1012
Skewness-0.6138191817
Sum1.1464932 × 1015
Variance1.481432767 × 1026
MonotonicityNot monotonic
2022-07-04T13:38:06.882793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.77779 × 10131
 
6.2%
5.17787 × 10131
 
6.2%
5.83375 × 10131
 
6.2%
6.40715 × 10131
 
6.2%
6.0781 × 10131
 
6.2%
6.65005 × 10131
 
6.2%
7.36715 × 10131
 
6.2%
7.53116 × 10131
 
6.2%
7.74432 × 10131
 
6.2%
7.95755 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.77779 × 10131
6.2%
5.17787 × 10131
6.2%
5.83375 × 10131
6.2%
6.0781 × 10131
6.2%
6.40715 × 10131
6.2%
6.65005 × 10131
6.2%
7.36715 × 10131
6.2%
7.51171 × 10131
6.2%
7.53116 × 10131
6.2%
7.63132 × 10131
6.2%
ValueCountFrequency (%)
8.75681 × 10131
6.2%
8.62743 × 10131
6.2%
8.4747 × 10131
6.2%
8.12246 × 10131
6.2%
7.95755 × 10131
6.2%
7.74432 × 10131
6.2%
7.63132 × 10131
6.2%
7.53116 × 10131
6.2%
7.51171 × 10131
6.2%
7.36715 × 10131
6.2%

Interactions

2022-07-04T13:38:00.217490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.010320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.892386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.852948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.895410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.893862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:32.857306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.890765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.784193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.777644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.657069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.671524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.644970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.688432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.649875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.589314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.576763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.432183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.333612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.309059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.305510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.108983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.982407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.942969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.987431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.984882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:33.020343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.980786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.875214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.863663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.744089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.759544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.733990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.850469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.738896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.679334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.662783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.523203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.421633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.399079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.399532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.202004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:25.078429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:27.035990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:29.084453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:31.079904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:33.118365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:35.072807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.969235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.952683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.837110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.852566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.828012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.944490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.832917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.773355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.753803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.613223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.514653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.493101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.491551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.287024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:25.169450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:27.128011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:29.175474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:31.171925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:33.212386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:35.162827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:37.128271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:39.039703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.928130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.943586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.925033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:47.041512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.922937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.862376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.840823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.701243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.675689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.581120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.588574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.377044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:25.263470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:27.307051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:29.268495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:31.265946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:33.311408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:35.253848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:37.223293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:39.130724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:41.022151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:43.039607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:45.031058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:47.135533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:49.014958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:51.021412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.930843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.793264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.769711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.674141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.751611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:23.467064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:25.358492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:27.403073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:29.361516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:31.358967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:33.406430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:35.345868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:37.317314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:39.223745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:41.186188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:43.131628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:45.138082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-04T13:37:50.119208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.039642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:53.987082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:55.887511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:57.783940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:59.756386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:01.780844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.509300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.462860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.449309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.501773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:32.467218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.520682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.421111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.337544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.296987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.226424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.266885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.295343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.290795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.212229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.128662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.072101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:55.973531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:57.871960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:59.848407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:01.873865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.611322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.558882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.539330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.596795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:32.566240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.612702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.510131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.493580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.384007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.320445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.355905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.390365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.379814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.304249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.219682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.160121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.061551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.029996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:59.939428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:01.966886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.704344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.660905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.632351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.694817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:32.660261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.705723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.602152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.589602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.476028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.413465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.450926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.487387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.468835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.398271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.389721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.249141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.151571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.123017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.032449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:02.126922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:24.798365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:26.757927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:28.799389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:30.793840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:32.763284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:34.797744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:36.692173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:38.681622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:40.565048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:42.574502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:44.544948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:46.585409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:48.557855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:50.493292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:52.483742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:54.341162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:56.241592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:37:58.216038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:38:00.124469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T13:38:06.993819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T13:38:07.204666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T13:38:07.346698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T13:38:07.488730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-04T13:38:02.267954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-04T13:38:02.435992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

yeardisputenon_violent_crisesviolent_criseslimited_warswarstotalgdp_gergdp_fragdp_itagdp_jpngdp_cangdp_rusgdp_usagdp_gbrgdp_bragdp_indgdp_mexgdp_zafgdp_chngdp_wld
01970-01-01 00:00:00.00000200563939026227428468600000002196950000000185822000000048314700000001173110000000764017000000130366000000002544830000000891634000000820382000000877476000000288868000000228597000000047777900000000
11970-01-01 00:00:00.00000200672114104306326299470000000023205400000001949550000000460166000000013192600000009899310000001381460000000027170600000001107630000000940260000000975387000000303861000000275213000000051778700000000
21970-01-01 00:00:00.00000200779126107266344342558000000026605900000002213100000000457975000000014688200000001299710000000144519000000003106180000000139711000000012167400000001052700000000333075000000355034000000058337500000000
31970-01-01 00:00:00.00000200882130102309353374526000000029303000000002408660000000510668000000015529900000001660850000000147128000000002938880000000169586000000011989000000001109990000000316132000000459431000000064071500000000
41970-01-01 00:00:00.00000200910711811025836834112600000002700890000000219993000000052894900000001374630000000122264000000014448900000000242580000000016670000000001341890000000900045000000329753000000510170000000060781000000000
51970-01-01 00:00:00.00000201095108139226370339967000000026451900000002136100000000575907000000016173400000001524920000000149921000000002491110000000220884000000016756200000001057800000000417365000000608716000000066500500000000
61970-01-01 00:00:00.000002011106871551920387374931000000028651600000002294990000000623315000000017933300000002045930000000155426000000002674890000000261616000000018230500000001180490000000458202000000755150000000073671500000000
71970-01-01 00:00:00.00000201299851772519405352714000000026836700000002086960000000627236000000018283700000002208300000000161970000000002719160000000246523000000018276400000001201090000000434401000000853223000000075311600000000
81970-01-01 00:00:00.000002013107821783120418373380000000028118800000002141920000000521233000000018466000000002292470000000167848000000002803290000000247282000000018567200000001274440000000400886000000957041000000077443200000000
91970-01-01 00:00:00.000002014978818125194103889090000000285596000000021620100000004896990000000180575000000020592400000001752720000000030871700000002456040000000203913000000013153500000003811990000001047570000000079575500000000

Last rows

yeardisputenon_violent_crisesviolent_criseslimited_warswarstotalgdp_gergdp_fragdp_itagdp_jpngdp_cangdp_rusgdp_usagdp_gbrgdp_bragdp_indgdp_mexgdp_zafgdp_chngdp_wld
61970-01-01 00:00:00.000002011106871551920387374931000000028651600000002294990000000623315000000017933300000002045930000000155426000000002674890000000261616000000018230500000001180490000000458202000000755150000000073671500000000
71970-01-01 00:00:00.00000201299851772519405352714000000026836700000002086960000000627236000000018283700000002208300000000161970000000002719160000000246523000000018276400000001201090000000434401000000853223000000075311600000000
81970-01-01 00:00:00.000002013107821783120418373380000000028118800000002141920000000521233000000018466000000002292470000000167848000000002803290000000247282000000018567200000001274440000000400886000000957041000000077443200000000
91970-01-01 00:00:00.000002014978818125194103889090000000285596000000021620100000004896990000000180575000000020592400000001752720000000030871700000002456040000000203913000000013153500000003811990000001047570000000079575500000000
101970-01-01 00:00:00.000002015908818324194043357590000000243919000000018366400000004444930000000155651000000013634800000001823830000000029565700000001802210000000210359000000011718700000003467100000001106160000000075117100000000
111970-01-01 00:00:00.000002016777218821183763469850000000247296000000018770700000005003680000000152799000000012767900000001874510000000027228500000001795690000000229480000000010784900000003235860000001123330000000076313200000000
121970-01-01 00:00:00.000002017687719016203713690850000000259515000000019618000000004930840000000164927000000015742000000001954300000000026990200000002063510000000265147000000011589100000003814490000001231040000000081224600000000
131970-01-01 00:00:00.000002018688317325163653977290000000279096000000020919300000005036890000000172533000000016573300000002061190000000029007900000001916930000000270111000000012224100000004048420000001389480000000086274300000000
141970-01-01 00:00:00.000002019719115823153583888330000000272887000000020093800000005148780000000174202000000016874500000002143320000000028786700000001877820000000287050000000012694300000003879350000001427990000000087568100000000
151970-01-01 00:00:00.000002020697018019213593846410000000263032000000018887100000005057760000000164542000000014835000000002095300000000027598000000001444730000000266025000000010739200000003354420000001472270000000084747000000000